{
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "sourceId": 3884,
     "sourceType": "datasetVersion",
     "datasetId": 2298
    }
   ],
   "dockerImageVersionId": 29852,
   "isInternetEnabled": false,
   "language": "python",
   "sourceType": "notebook",
   "isGpuEnabled": false
  },
  "colab": {
   "provenance": [],
   "include_colab_link": true
  }
 },
 "nbformat_minor": 0,
 "nbformat": 4,
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view-in-github",
    "colab_type": "text"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/IgorLytkin/MyDataSpell/blob/main/%D0%9B%D0%B5%D0%BA%D1%86%D0%B8%D1%8F_8_9.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Лекция 8-9. Деревья и ансамбли."
   ],
   "metadata": {
    "id": "zBtTzglEcexm"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Как нам выбрать оптимальные условия разбиения в деревьях?\n",
    "Энтропия — это то, как много информации вам не известно о системе."
   ],
   "metadata": {
    "id": "AH4kHu6qeXe6"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "![image.png]()"
   ],
   "metadata": {
    "id": "m0blHRcvc1OM"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "![Entropy](http://www.learnbymarketing.com/wp-content/uploads/2016/02/entropy-formula.png)\n"
   ],
   "metadata": {
    "id": "u9ixS7rB3_Wf"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "![Gini index](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRzYHkcmZKKp2sJN1HpHvw-NgqbD9EnapnbXozXRgajrSGvEnYy&s)"
   ],
   "metadata": {
    "id": "u4eI0-t1Flxd"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt # data visualization\n",
    "%matplotlib inline\n"
   ],
   "metadata": {
    "trusted": true,
    "id": "Lf7d3fa-3_Wi",
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:08.180924900Z",
     "start_time": "2024-02-08T22:54:07.686308500Z"
    }
   },
   "execution_count": 47,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "data = 'car_evaluation.csv'\n",
    "\n",
    "df = pd.read_csv(data, header=None)"
   ],
   "metadata": {
    "trusted": true,
    "id": "vtWSHxGZ3_Wm",
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:08.274107900Z",
     "start_time": "2024-02-08T22:54:08.186799Z"
    }
   },
   "execution_count": 48,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# view dimensions of dataset\n",
    "\n",
    "df.shape"
   ],
   "metadata": {
    "trusted": true,
    "id": "6EqQg5qi3_Wm",
    "outputId": "9d79d366-e215-4651-a0a2-39e829129691",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:08.338214500Z",
     "start_time": "2024-02-08T22:54:08.273023900Z"
    }
   },
   "execution_count": 49,
   "outputs": [
    {
     "data": {
      "text/plain": "(1728, 7)"
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# preview the dataset\n",
    "\n",
    "df.head()"
   ],
   "metadata": {
    "trusted": true,
    "id": "JF4rX2Xr3_Wn",
    "outputId": "c65d672d-4175-4c4c-b210-6251bdeca95b",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:08.439685400Z",
     "start_time": "2024-02-08T22:54:08.331762400Z"
    }
   },
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "       0      1  2  3      4     5      6\n0  vhigh  vhigh  2  2  small   low  unacc\n1  vhigh  vhigh  2  2  small   med  unacc\n2  vhigh  vhigh  2  2  small  high  unacc\n3  vhigh  vhigh  2  2    med   low  unacc\n4  vhigh  vhigh  2  2    med   med  unacc",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>vhigh</td>\n      <td>vhigh</td>\n      <td>2</td>\n      <td>2</td>\n      <td>small</td>\n      <td>low</td>\n      <td>unacc</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>vhigh</td>\n      <td>vhigh</td>\n      <td>2</td>\n      <td>2</td>\n      <td>small</td>\n      <td>med</td>\n      <td>unacc</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>vhigh</td>\n      <td>vhigh</td>\n      <td>2</td>\n      <td>2</td>\n      <td>small</td>\n      <td>high</td>\n      <td>unacc</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>vhigh</td>\n      <td>vhigh</td>\n      <td>2</td>\n      <td>2</td>\n      <td>med</td>\n      <td>low</td>\n      <td>unacc</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>vhigh</td>\n      <td>vhigh</td>\n      <td>2</td>\n      <td>2</td>\n      <td>med</td>\n      <td>med</td>\n      <td>unacc</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Rename column names\n",
    "\n",
    "We can see that the dataset does not have proper column names. The columns are merely labelled as 0,1,2.... and so on. We should give proper names to the columns. I will do it as follows:-\n",
    "\n",
    "Мы видим, что в наборе данных нет собственных имен столбцов. Столбцы просто помечены как 0,1,2.... и так далее. Мы должны присвоить столбцам собственные имена. Я сделаю это следующим образом:-"
   ],
   "metadata": {
    "id": "AC0NTa8I3_Wn"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# col_names = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class']\n",
    "col_names = [\"покупка\", \"обслуживание\", \"двери\", \"люди\", \"багажник\", \"безопасность\", \"класс\"]\n",
    "\n",
    "\n",
    "df.columns = col_names\n",
    "\n",
    "col_names"
   ],
   "metadata": {
    "trusted": true,
    "id": "flvbGFDH3_Wn",
    "outputId": "70356390-1dcb-4dc8-f27a-aeaaf2357032",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:08.467449Z",
     "start_time": "2024-02-08T22:54:08.361206700Z"
    }
   },
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "['покупка',\n 'обслуживание',\n 'двери',\n 'люди',\n 'багажник',\n 'безопасность',\n 'класс']"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# let's again preview the dataset\n",
    "\n",
    "df.head()"
   ],
   "metadata": {
    "trusted": true,
    "id": "VLZr9kBq3_Wo",
    "outputId": "02ab2de2-11f1-4846-e4a9-ea260acb0a2a",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:08.540835700Z",
     "start_time": "2024-02-08T22:54:08.455938Z"
    }
   },
   "execution_count": 52,
   "outputs": [
    {
     "data": {
      "text/plain": "  покупка обслуживание двери люди багажник безопасность  класс\n0   vhigh        vhigh     2    2    small          low  unacc\n1   vhigh        vhigh     2    2    small          med  unacc\n2   vhigh        vhigh     2    2    small         high  unacc\n3   vhigh        vhigh     2    2      med          low  unacc\n4   vhigh        vhigh     2    2      med          med  unacc",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>покупка</th>\n      <th>обслуживание</th>\n      <th>двери</th>\n      <th>люди</th>\n      <th>багажник</th>\n      <th>безопасность</th>\n      <th>класс</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>vhigh</td>\n      <td>vhigh</td>\n      <td>2</td>\n      <td>2</td>\n      <td>small</td>\n      <td>low</td>\n      <td>unacc</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>vhigh</td>\n      <td>vhigh</td>\n      <td>2</td>\n      <td>2</td>\n      <td>small</td>\n      <td>med</td>\n      <td>unacc</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>vhigh</td>\n      <td>vhigh</td>\n      <td>2</td>\n      <td>2</td>\n      <td>small</td>\n      <td>high</td>\n      <td>unacc</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>vhigh</td>\n      <td>vhigh</td>\n      <td>2</td>\n      <td>2</td>\n      <td>med</td>\n      <td>low</td>\n      <td>unacc</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>vhigh</td>\n      <td>vhigh</td>\n      <td>2</td>\n      <td>2</td>\n      <td>med</td>\n      <td>med</td>\n      <td>unacc</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "df.info()"
   ],
   "metadata": {
    "trusted": true,
    "id": "BgTTW6aV3_Wo",
    "outputId": "a966644d-9458-4338-f6c1-0aa8563fb5f5",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:08.813878800Z",
     "start_time": "2024-02-08T22:54:08.511025800Z"
    }
   },
   "execution_count": 53,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1728 entries, 0 to 1727\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   покупка       1728 non-null   object\n",
      " 1   обслуживание  1728 non-null   object\n",
      " 2   двери         1728 non-null   object\n",
      " 3   люди          1728 non-null   object\n",
      " 4   багажник      1728 non-null   object\n",
      " 5   безопасность  1728 non-null   object\n",
      " 6   класс         1728 non-null   object\n",
      "dtypes: object(7)\n",
      "memory usage: 94.6+ KB\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# col_names = ['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety', 'class']\n",
    "col_names = [\"покупка\", \"обслуживание\", \"двери\", \"люди\", \"багажник\", \"безопасность\", \"класс\"]\n",
    "\n",
    "\n",
    "for col in col_names:\n",
    "    print(df[col].value_counts())"
   ],
   "metadata": {
    "trusted": true,
    "id": "S4ru_rK23_Wp",
    "outputId": "5653321e-4cd5-4112-80ae-96a437f8eb7d",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:08.953206200Z",
     "start_time": "2024-02-08T22:54:08.797276600Z"
    }
   },
   "execution_count": 54,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "покупка\n",
      "vhigh    432\n",
      "high     432\n",
      "med      432\n",
      "low      432\n",
      "Name: count, dtype: int64\n",
      "обслуживание\n",
      "vhigh    432\n",
      "high     432\n",
      "med      432\n",
      "low      432\n",
      "Name: count, dtype: int64\n",
      "двери\n",
      "2        432\n",
      "3        432\n",
      "4        432\n",
      "5more    432\n",
      "Name: count, dtype: int64\n",
      "люди\n",
      "2       576\n",
      "4       576\n",
      "more    576\n",
      "Name: count, dtype: int64\n",
      "багажник\n",
      "small    576\n",
      "med      576\n",
      "big      576\n",
      "Name: count, dtype: int64\n",
      "безопасность\n",
      "low     576\n",
      "med     576\n",
      "high    576\n",
      "Name: count, dtype: int64\n",
      "класс\n",
      "unacc    1210\n",
      "acc       384\n",
      "good       69\n",
      "vgood      65\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can see that the `doors` and `persons` are categorical in nature. So, I will treat them as categorical variables.\n",
    "Мы можем видеть, что \"двери\" и \"люди\" являются категориальными по своей природе. Итак, я буду рассматривать их как категориальные переменные."
   ],
   "metadata": {
    "id": "CRj2eVjw3_Wp"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Summary of variables\n",
    "### Краткое описание переменных\n",
    "\n",
    "- There are 7 variables in the dataset. All the variables are of categorical data type.\n",
    "- В наборе данных 7 переменных. Все переменные относятся к категориальному типу данных.\n",
    "- \n",
    "- These are given by `buying`, `maint`, `doors`, `persons`, `lug_boot`, `safety` and `class`.\n",
    "- - Они указаны в разделах `покупка`, `обслуживание`, `двери`, `люди`, `багажник`, `безопасность` и `класс`.\n",
    "\n",
    "- `class` is the target variable.\n",
    "- `class` - это целевая переменная."
   ],
   "metadata": {
    "id": "j_4jufWI3_Wp"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "df['класс'].value_counts()"
   ],
   "metadata": {
    "trusted": true,
    "id": "WsEdbhAt3_Wq",
    "outputId": "dad2e36b-d519-4c51-be47-265cd8ad713d",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:09.170691900Z",
     "start_time": "2024-02-08T22:54:08.938767600Z"
    }
   },
   "execution_count": 55,
   "outputs": [
    {
     "data": {
      "text/plain": "класс\nunacc    1210\nacc       384\ngood       69\nvgood      65\nName: count, dtype: int64"
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# check missing values in variables\n",
    "# проверьте пропущенные значения в переменных\n",
    "\n",
    "df.isnull().sum()"
   ],
   "metadata": {
    "trusted": true,
    "id": "oLLJpICL3_Wz",
    "outputId": "eb0fa5b1-f023-4acd-e016-e53680a304f0",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:09.218728300Z",
     "start_time": "2024-02-08T22:54:09.141114200Z"
    }
   },
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "покупка         0\nобслуживание    0\nдвери           0\nлюди            0\nбагажник        0\nбезопасность    0\nкласс           0\ndtype: int64"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "X = df.drop(['класс'], axis=1)\n",
    "\n",
    "y = df['класс']"
   ],
   "metadata": {
    "trusted": true,
    "id": "tRWOneIq3_W0",
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:09.345637900Z",
     "start_time": "2024-02-08T22:54:09.224400500Z"
    }
   },
   "execution_count": 57,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# split X and y into training and testing sets\n",
    "# разделите X и y на обучающие и тестовые наборы\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n"
   ],
   "metadata": {
    "trusted": true,
    "id": "GqG96JtK3_W0",
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:09.417312500Z",
     "start_time": "2024-02-08T22:54:09.325657900Z"
    }
   },
   "execution_count": 58,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# check the shape of X_train and X_test\n",
    "\n",
    "X_train.shape, X_test.shape"
   ],
   "metadata": {
    "trusted": true,
    "id": "6FsxZ7_X3_W0",
    "outputId": "e43ca5d6-738d-4648-adf2-67317d2cd7d5",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:09.464440600Z",
     "start_time": "2024-02-08T22:54:09.421674800Z"
    }
   },
   "execution_count": 59,
   "outputs": [
    {
     "data": {
      "text/plain": "((1382, 6), (346, 6))"
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# check data types in X_train\n",
    "\n",
    "X_train.dtypes"
   ],
   "metadata": {
    "trusted": true,
    "id": "msXtya4l3_W1",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "38607804-6a91-4ab0-a478-35b1d897acc0",
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:09.516240600Z",
     "start_time": "2024-02-08T22:54:09.450544800Z"
    }
   },
   "execution_count": 60,
   "outputs": [
    {
     "data": {
      "text/plain": "покупка         object\nобслуживание    object\nдвери           object\nлюди            object\nбагажник        object\nбезопасность    object\ndtype: object"
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "X_train.head()"
   ],
   "metadata": {
    "trusted": true,
    "id": "mlvRzFTy3_W1",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "outputId": "47af733f-be9a-4b22-8335-01cb1f50db68",
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:09.556187500Z",
     "start_time": "2024-02-08T22:54:09.500916400Z"
    }
   },
   "execution_count": 61,
   "outputs": [
    {
     "data": {
      "text/plain": "     покупка обслуживание  двери  люди багажник безопасность\n107    vhigh        vhigh  5more  more      big         high\n901      med        vhigh      3     4    small          med\n1709     low          low  5more     2      big         high\n706     high          med      4     2      med          med\n678     high          med      3     2      med          low",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>покупка</th>\n      <th>обслуживание</th>\n      <th>двери</th>\n      <th>люди</th>\n      <th>багажник</th>\n      <th>безопасность</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>107</th>\n      <td>vhigh</td>\n      <td>vhigh</td>\n      <td>5more</td>\n      <td>more</td>\n      <td>big</td>\n      <td>high</td>\n    </tr>\n    <tr>\n      <th>901</th>\n      <td>med</td>\n      <td>vhigh</td>\n      <td>3</td>\n      <td>4</td>\n      <td>small</td>\n      <td>med</td>\n    </tr>\n    <tr>\n      <th>1709</th>\n      <td>low</td>\n      <td>low</td>\n      <td>5more</td>\n      <td>2</td>\n      <td>big</td>\n      <td>high</td>\n    </tr>\n    <tr>\n      <th>706</th>\n      <td>high</td>\n      <td>med</td>\n      <td>4</td>\n      <td>2</td>\n      <td>med</td>\n      <td>med</td>\n    </tr>\n    <tr>\n      <th>678</th>\n      <td>high</td>\n      <td>med</td>\n      <td>3</td>\n      <td>2</td>\n      <td>med</td>\n      <td>low</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# import category encoders\n",
    "!pip install category_encoders\n",
    "import category_encoders as ce"
   ],
   "metadata": {
    "trusted": true,
    "id": "AP8KFlq_3_W2",
    "outputId": "52964461-f7b6-4088-e81e-73ad25005c6c",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:18.526091600Z",
     "start_time": "2024-02-08T22:54:09.559093700Z"
    }
   },
   "execution_count": 62,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: category_encoders in c:\\users\\igorl\\appdata\\roaming\\python\\python311\\site-packages (2.6.3)\n",
      "Requirement already satisfied: numpy>=1.14.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from category_encoders) (1.26.3)\n",
      "Requirement already satisfied: scikit-learn>=0.20.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from category_encoders) (1.2.2)\n",
      "Requirement already satisfied: scipy>=1.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from category_encoders) (1.11.4)\n",
      "Requirement already satisfied: statsmodels>=0.9.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from category_encoders) (0.14.0)\n",
      "Requirement already satisfied: pandas>=1.0.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from category_encoders) (2.1.4)\n",
      "Requirement already satisfied: patsy>=0.5.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from category_encoders) (0.5.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=1.0.5->category_encoders) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=1.0.5->category_encoders) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas>=1.0.5->category_encoders) (2023.3)\n",
      "Requirement already satisfied: six in c:\\programdata\\anaconda3\\lib\\site-packages (from patsy>=0.5.1->category_encoders) (1.16.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20.0->category_encoders) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20.0->category_encoders) (2.2.0)\n",
      "Requirement already satisfied: packaging>=21.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from statsmodels>=0.9.0->category_encoders) (23.1)\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "![image.png]()"
   ],
   "metadata": {
    "id": "dU1m_7lOgHXB"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# encode variables with ordinal encoding\n",
    "# encoder = ce.OrdinalEncoder(cols=['buying', 'maint', 'doors', 'persons', 'lug_boot', 'safety'])\n",
    "\n",
    "#кодируйте переменные с помощью порядковой кодировки\n",
    "encoder = ce.OrdinalEncoder(cols=[\"покупка\", \"обслуживание\", \"двери\", \"люди\", \"багажник\", \"безопасность\"])\n",
    "\n",
    "X_train = encoder.fit_transform(X_train)\n",
    "\n",
    "X_test = encoder.transform(X_test)"
   ],
   "metadata": {
    "trusted": true,
    "id": "cjG2BBKI3_W2",
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:18.586550400Z",
     "start_time": "2024-02-08T22:54:18.517777800Z"
    }
   },
   "execution_count": 63,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "X_train.head()"
   ],
   "metadata": {
    "trusted": true,
    "id": "gZGGZGa33_W2",
    "outputId": "4065139f-aba9-4aff-f2b1-fe9067fd99cd",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:18.589873500Z",
     "start_time": "2024-02-08T22:54:18.560615500Z"
    }
   },
   "execution_count": 64,
   "outputs": [
    {
     "data": {
      "text/plain": "      покупка  обслуживание  двери  люди  багажник  безопасность\n107         1             1      1     1         1             1\n901         2             1      2     2         2             2\n1709        3             2      1     3         1             1\n706         4             3      3     3         3             2\n678         4             3      2     3         3             3",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>покупка</th>\n      <th>обслуживание</th>\n      <th>двери</th>\n      <th>люди</th>\n      <th>багажник</th>\n      <th>безопасность</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>107</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>901</th>\n      <td>2</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1709</th>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>706</th>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>678</th>\n      <td>4</td>\n      <td>3</td>\n      <td>2</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "X_test.head()"
   ],
   "metadata": {
    "trusted": true,
    "id": "z2X6EcKM3_W2",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "outputId": "32c4054e-3e8b-46f8-c100-6db2aa04a7a3",
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:18.611832800Z",
     "start_time": "2024-02-08T22:54:18.575714Z"
    }
   },
   "execution_count": 65,
   "outputs": [
    {
     "data": {
      "text/plain": "      покупка  обслуживание  двери  люди  багажник  безопасность\n599         4             4      3     3         3             1\n1201        2             2      4     2         3             2\n628         4             4      1     3         1             2\n1498        3             4      1     2         3             2\n1263        2             2      3     1         3             3",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>покупка</th>\n      <th>обслуживание</th>\n      <th>двери</th>\n      <th>люди</th>\n      <th>багажник</th>\n      <th>безопасность</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>599</th>\n      <td>4</td>\n      <td>4</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1201</th>\n      <td>2</td>\n      <td>2</td>\n      <td>4</td>\n      <td>2</td>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>628</th>\n      <td>4</td>\n      <td>4</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1498</th>\n      <td>3</td>\n      <td>4</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1263</th>\n      <td>2</td>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n"
   ],
   "metadata": {
    "trusted": true,
    "id": "kKTdaclz3_W3",
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:18.687163200Z",
     "start_time": "2024-02-08T22:54:18.588773200Z"
    }
   },
   "execution_count": 66,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# instantiate the DecisionTreeClassifier model with criterion gini index\n",
    "\n",
    "clf_gini = DecisionTreeClassifier(criterion='gini', max_depth=3, random_state=0)\n",
    "\n",
    "\n",
    "# fit the model\n",
    "clf_gini.fit(X_train, y_train)\n"
   ],
   "metadata": {
    "trusted": true,
    "id": "ia18v2PN3_W3",
    "outputId": "aba2660a-6d67-4320-d827-c7628cc94a22",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:18.722971600Z",
     "start_time": "2024-02-08T22:54:18.596909400Z"
    }
   },
   "execution_count": 67,
   "outputs": [
    {
     "data": {
      "text/plain": "DecisionTreeClassifier(max_depth=3, random_state=0)",
      "text/html": "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3, random_state=0)</pre></div></div></div></div></div>"
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Predict the Test set results with criterion gini index\n",
    "### Прогнозирование результатов набора тестов с использованием критерия индекса Джини"
   ],
   "metadata": {
    "id": "KV6yvqPU3_W3"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "y_pred_gini = clf_gini.predict(X_test)\n"
   ],
   "metadata": {
    "trusted": true,
    "id": "k5EtlspD3_W4",
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:18.732584500Z",
     "start_time": "2024-02-08T22:54:18.615124100Z"
    }
   },
   "execution_count": 68,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Check accuracy score with criterion gini index\n",
    "### Проверьте оценку точности с помощью критерия индекса Джини"
   ],
   "metadata": {
    "id": "_EF0OJ-O3_W4"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# print('Model accuracy score with criterion gini index: {0:0.4f}'. format(accuracy_score(y_test, y_pred_gini)))\n",
    "print('Оценка точности модели по критерию индекса Джини: {0:0.4f}'. format(accuracy_score(y_test, y_pred_gini)))"
   ],
   "metadata": {
    "trusted": true,
    "id": "6GIzeha63_W4",
    "outputId": "4374835b-7b4c-4823-c877-354226c7999d",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:18.734763400Z",
     "start_time": "2024-02-08T22:54:18.626445Z"
    }
   },
   "execution_count": 69,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Оценка точности модели по критерию индекса Джини: 0.8179\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "y_pred_train_gini = clf_gini.predict(X_train)\n",
    "\n",
    "y_pred_train_gini"
   ],
   "metadata": {
    "trusted": true,
    "id": "4IWzGlkZ3_W5",
    "outputId": "0965920d-4d27-4f1c-a9f9-944859e225e0",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:18.826954200Z",
     "start_time": "2024-02-08T22:54:18.637959900Z"
    }
   },
   "execution_count": 70,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['unacc', 'unacc', 'unacc', ..., 'acc', 'unacc', 'acc'],\n      dtype=object)"
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print('Training-set accuracy score: {0:0.4f}'. format(accuracy_score(y_train, y_pred_train_gini)))"
   ],
   "metadata": {
    "trusted": true,
    "id": "23-ecM9r3_W5",
    "outputId": "a5ee0978-3e22-4ed0-e307-9dfb87254b86",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:18.920553300Z",
     "start_time": "2024-02-08T22:54:18.657996600Z"
    }
   },
   "execution_count": 71,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training-set accuracy score: 0.8025\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# print the scores on training and test set\n",
    "\n",
    "print('Training set score: {:.4f}'.format(clf_gini.score(X_train, y_train)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(clf_gini.score(X_test, y_test)))"
   ],
   "metadata": {
    "trusted": true,
    "id": "bjYYr1la3_W5",
    "outputId": "6f711e0d-d45d-4101-9b43-1ebbfbd8298d",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:18.922554300Z",
     "start_time": "2024-02-08T22:54:18.668013400Z"
    }
   },
   "execution_count": 72,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.8025\n",
      "Test set score: 0.8179\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Visualize decision-trees\n",
    "### Визуализируйте деревья принятия решений"
   ],
   "metadata": {
    "id": "p7VWqc_R3_W5"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "from sklearn import tree\n",
    "\n",
    "tree.plot_tree(clf_gini.fit(X_train, y_train))"
   ],
   "metadata": {
    "trusted": true,
    "id": "-5BzDnhO3_W6",
    "outputId": "ac03cb60-ff23-47fa-b0a0-abb79815cc73",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 774
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:19.454082600Z",
     "start_time": "2024-02-08T22:54:18.683846500Z"
    }
   },
   "execution_count": 73,
   "outputs": [
    {
     "data": {
      "text/plain": "[Text(0.6666666666666666, 0.875, 'x[5] <= 2.5\\ngini = 0.452\\nsamples = 1382\\nvalue = [301, 58, 975, 48]'),\n Text(0.5, 0.625, 'x[3] <= 2.5\\ngini = 0.577\\nsamples = 913\\nvalue = [301, 58, 506, 48]'),\n Text(0.3333333333333333, 0.375, 'x[1] <= 1.5\\ngini = 0.631\\nsamples = 615\\nvalue = [301, 58, 208, 48]'),\n Text(0.16666666666666666, 0.125, 'gini = 0.462\\nsamples = 149\\nvalue = [54, 0, 95, 0]'),\n Text(0.5, 0.125, 'gini = 0.634\\nsamples = 466\\nvalue = [247, 58, 113, 48]'),\n Text(0.6666666666666666, 0.375, 'gini = 0.0\\nsamples = 298\\nvalue = [0, 0, 298, 0]'),\n Text(0.8333333333333334, 0.625, 'gini = 0.0\\nsamples = 469\\nvalue = [0, 0, 469, 0]')]"
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Visualize decision-trees with graphviz\n",
    "### Визуализируйте деревья решений с помощью graphviz"
   ],
   "metadata": {
    "id": "okSKWTld3_W6"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import graphviz\n",
    "dot_data = tree.export_graphviz(clf_gini, out_file=None,\n",
    "                              feature_names=X_train.columns,\n",
    "                              class_names=y_train,\n",
    "                              filled=True, rounded=True,\n",
    "                              special_characters=True)\n",
    "\n",
    "graph = graphviz.Source(dot_data)\n",
    "\n",
    "graph"
   ],
   "metadata": {
    "trusted": true,
    "id": "1vwjjsEk3_W6",
    "outputId": "5d27d6f7-28d5-40e8-9cd2-9d8b131bb5d1",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 599
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:19.557043600Z",
     "start_time": "2024-02-08T22:54:19.452966800Z"
    }
   },
   "execution_count": 74,
   "outputs": [
    {
     "data": {
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text-anchor=\"start\" x=\"267.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">безопасность ≤ 2.5</text>\n<text text-anchor=\"start\" x=\"292.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.452</text>\n<text text-anchor=\"start\" x=\"279.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1382</text>\n<text text-anchor=\"start\" x=\"251\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [301, 58, 975, 48]</text>\n<text text-anchor=\"start\" x=\"286.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = unacc</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<path fill=\"#bddef6\" stroke=\"black\" d=\"M315,-306C315,-306 169,-306 169,-306 163,-306 157,-300 157,-294 157,-294 157,-235 157,-235 157,-229 163,-223 169,-223 169,-223 315,-223 315,-223 321,-223 327,-229 327,-235 327,-235 327,-294 327,-294 327,-300 321,-306 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font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.631</text>\n<text text-anchor=\"start\" x=\"111\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 615</text>\n<text text-anchor=\"start\" x=\"79\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [301, 58, 208, 48]</text>\n<text text-anchor=\"start\" x=\"114.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = unacc</text>\n</g>\n<!-- 1&#45;&gt;2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>1&#45;&gt;2</title>\n<path fill=\"none\" stroke=\"black\" d=\"M212.16,-222.91C205.63,-214.01 198.64,-204.51 191.89,-195.33\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"194.53,-193.01 185.78,-187.02 188.89,-197.15 194.53,-193.01\"/>\n</g>\n<!-- 5 -->\n<g id=\"node6\" class=\"node\">\n<title>5</title>\n<path fill=\"#399de5\" stroke=\"black\" d=\"M387,-179.5C387,-179.5 271,-179.5 271,-179.5 265,-179.5 259,-173.5 259,-167.5 259,-167.5 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295.56,-185.6 301.18,-189.78\"/>\n</g>\n<!-- 3 -->\n<g id=\"node4\" class=\"node\">\n<title>3</title>\n<path fill=\"#aad5f4\" stroke=\"black\" d=\"M128,-68C128,-68 12,-68 12,-68 6,-68 0,-62 0,-56 0,-56 0,-12 0,-12 0,-6 6,0 12,0 12,0 128,0 128,0 134,0 140,-6 140,-12 140,-12 140,-56 140,-56 140,-62 134,-68 128,-68\"/>\n<text text-anchor=\"start\" x=\"34.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.462</text>\n<text text-anchor=\"start\" x=\"25\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 149</text>\n<text text-anchor=\"start\" x=\"8\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [54, 0, 95, 0]</text>\n<text text-anchor=\"start\" x=\"28.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = unacc</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M123.98,-103.73C116.96,-94.79 109.52,-85.32 102.48,-76.36\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"105.08,-74 96.15,-68.3 99.58,-78.33 105.08,-74\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"#f5cfb4\" stroke=\"black\" d=\"M315.5,-68C315.5,-68 170.5,-68 170.5,-68 164.5,-68 158.5,-62 158.5,-56 158.5,-56 158.5,-12 158.5,-12 158.5,-6 164.5,0 170.5,0 170.5,0 315.5,0 315.5,0 321.5,0 327.5,-6 327.5,-12 327.5,-12 327.5,-56 327.5,-56 327.5,-62 321.5,-68 315.5,-68\"/>\n<text text-anchor=\"start\" x=\"207.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.634</text>\n<text text-anchor=\"start\" x=\"198\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 466</text>\n<text text-anchor=\"start\" x=\"166.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [247, 58, 113, 48]</text>\n<text text-anchor=\"start\" x=\"201.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = unacc</text>\n</g>\n<!-- 2&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>2&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M188.4,-103.73C195.5,-94.79 203.02,-85.32 210.14,-76.36\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"213.06,-78.31 216.54,-68.3 207.58,-73.95 213.06,-78.31\"/>\n</g>\n</g>\n</svg>\n",
      "text/plain": "<graphviz.sources.Source at 0x197d4f9c6d0>"
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# instantiate the DecisionTreeClassifier model with criterion entropy\n",
    "\n",
    "clf_en = DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)\n",
    "\n",
    "\n",
    "# fit the model\n",
    "clf_en.fit(X_train, y_train)"
   ],
   "metadata": {
    "trusted": true,
    "id": "6hYf7NH53_W6",
    "outputId": "28d5c35c-0ca1-4390-f9c2-48e88b2f67ef",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 74
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:19.577034Z",
     "start_time": "2024-02-08T22:54:19.558150500Z"
    }
   },
   "execution_count": 75,
   "outputs": [
    {
     "data": {
      "text/plain": "DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)",
      "text/html": "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, random_state=0)</pre></div></div></div></div></div>"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Predict the Test set results with criterion entropy\n",
    "### Прогнозирование результатов тестового набора с использованием критерия энтропии "
   ],
   "metadata": {
    "id": "SVsjgJNF3_W7"
   }
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Data Source is not selected",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mException\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[76], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m(\u001B[38;5;28m__import__\u001B[39m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbase64\u001B[39m\u001B[38;5;124m'\u001B[39m)\u001B[38;5;241m.\u001B[39mb64decode(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mRGF0YSBTb3VyY2UgaXMgbm90IHNlbGVjdGVk\u001B[39m\u001B[38;5;124m'\u001B[39m)\u001B[38;5;241m.\u001B[39mdecode(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mutf-8\u001B[39m\u001B[38;5;124m'\u001B[39m))\n",
      "\u001B[1;31mException\u001B[0m: Data Source is not selected"
     ]
    }
   ],
   "source": [
    "%%sql\n"
   ],
   "metadata": {
    "collapsed": false,
    "SqlCellData": {
     "variableName$1": "df_sql"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:19.650607400Z",
     "start_time": "2024-02-08T22:54:19.577034Z"
    }
   },
   "execution_count": 76
  },
  {
   "cell_type": "code",
   "source": [
    "y_pred_en = clf_en.predict(X_test)"
   ],
   "metadata": {
    "trusted": true,
    "id": "w8dNoLPZ3_W7",
    "ExecuteTime": {
     "start_time": "2024-02-08T22:54:19.642608100Z"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Check accuracy score with criterion entropy\n",
    "### Проверьте оценку точности с помощью критерия энтропии"
   ],
   "metadata": {
    "id": "xH7RTAZ43_W7"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print('Model accuracy score with criterion entropy: {0:0.4f}'. format(accuracy_score(y_test, y_pred_en)))"
   ],
   "metadata": {
    "trusted": true,
    "id": "2HSg5a1J3_W7",
    "outputId": "702f757a-80af-498a-cd82-84e2a054e090",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "start_time": "2024-02-08T22:54:19.646609Z"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Compare the train-set and test-set accuracy\n",
    "### Сравните точность тренировочного набора и тестового набора\n",
    "\n",
    "\n",
    "Now, I will compare the train-set and test-set accuracy to check for overfitting.\n",
    "Теперь я сравню точность тренировочного набора и тестового набора, чтобы проверить, нет ли переобучения."
   ],
   "metadata": {
    "id": "qRtyn6om3_W7"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "y_pred_train_en = clf_en.predict(X_train)\n",
    "\n",
    "y_pred_train_en"
   ],
   "metadata": {
    "trusted": true,
    "id": "0a8jXEf63_W7",
    "outputId": "1bb1c9d4-cae7-4675-8ede-4f0a1a7ebcac",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "start_time": "2024-02-08T22:54:19.649608700Z"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "print('Training-set accuracy score: {0:0.4f}'. format(accuracy_score(y_train, y_pred_train_en)))"
   ],
   "metadata": {
    "trusted": true,
    "id": "N8rjjir43_W8",
    "outputId": "f1c34ff0-f88e-4a44-9193-f33489ff6782",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "end_time": "2024-02-08T22:54:19.792534500Z",
     "start_time": "2024-02-08T22:54:19.652606500Z"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Check for overfitting and underfitting\n",
    "### Проверьте, нет ли переобучения и недостаточной подгонки"
   ],
   "metadata": {
    "id": "IaHc-K0H3_W8"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# print the scores on training and test set\n",
    "\n",
    "print('Training set score: {:.4f}'.format(clf_en.score(X_train, y_train)))\n",
    "\n",
    "print('Test set score: {:.4f}'.format(clf_en.score(X_test, y_test)))"
   ],
   "metadata": {
    "trusted": true,
    "id": "bYWjJiba3_W8",
    "outputId": "1ba72a1b-af8c-4feb-dd13-3f217af9da9b",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "ExecuteTime": {
     "start_time": "2024-02-08T22:54:19.656621400Z"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Visualize decision-trees\n",
    "### Визуализируйте деревья принятия решений"
   ],
   "metadata": {
    "id": "CUUmZZlu3_W9"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "from sklearn import tree\n",
    "\n",
    "tree.plot_tree(clf_en.fit(X_train, y_train))"
   ],
   "metadata": {
    "trusted": true,
    "id": "gVh6Nmwg3_W9",
    "outputId": "59481c71-689d-4654-a516-2ef3cf7b2a20",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 774
    },
    "ExecuteTime": {
     "start_time": "2024-02-08T22:54:19.662624400Z"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Visualize decision-trees with graphviz\n",
    "### Визуализируйте деревья решений с помощью graphviz"
   ],
   "metadata": {
    "id": "vyrTRH_C3_W9"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import graphviz\n",
    "dot_data = tree.export_graphviz(clf_en, out_file=None,\n",
    "                              feature_names=X_train.columns,\n",
    "                              class_names=y_train,\n",
    "                              filled=True, rounded=True,\n",
    "                              special_characters=True)\n",
    "\n",
    "graph = graphviz.Source(dot_data)\n",
    "\n",
    "graph"
   ],
   "metadata": {
    "trusted": true,
    "id": "aztxbQ7P3_W9",
    "outputId": "3ce44411-f65a-4eb5-c100-32eeb5f5814b",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 599
    },
    "ExecuteTime": {
     "start_time": "2024-02-08T22:54:19.664621800Z"
    }
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, y_pred_en))"
   ],
   "metadata": {
    "scrolled": true,
    "trusted": true,
    "id": "VO46pvJu3_W-",
    "outputId": "6ef8ac34-64ec-4af6-9102-983442475772",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 194
    },
    "ExecuteTime": {
     "start_time": "2024-02-08T22:54:19.668138500Z"
    }
   },
   "execution_count": null,
   "outputs": []
  }
 ]
}
